Intraoperative Hypotension Is Associated With Adverse Clinical Outcomes After Noncardiac Surgery.

BACKGROUND: Intraoperative hypotension (IOH) occurs frequently during surgery and may be associated with organ ischemia; however, few multicenter studies report data regarding its associations with adverse postoperative outcomes across varying hemodynamic thresholds. Additionally, no study has evaluated the association between IOH exposure and adverse outcomes among patients by various age groups.
METHODS: A multicenter retrospective cohort study was conducted between 2008 and 2017 using intraoperative blood pressure data from the US electronic health records database to examine postoperative outcomes. IOH was assessed in 368,222 noncardiac surgical procedures using 5 methods: (a) absolute maximum decrease in mean arterial pressure (MAP) during surgery, (b) time under each absolute threshold, (c) total area under each threshold, (d) time-weighted average MAP under each threshold, and (e) cumulative time under the prespecified relative MAP thresholds. MAP thresholds were defined by absolute limits (≤75, ≤65, ≤55 mm Hg) and by relative limits (20% and 40% lower than baseline). The primary outcome was major adverse cardiac or cerebrovascular events; secondary outcomes were all-cause 30- and 90-day mortality, 30-day acute myocardial injury, and 30-day acute ischemic stroke. Residual confounding was minimized by controlling for observable patient and surgical factors. In addition, we stratified patients into age subgroups (18-40, 41-50, 51-60, 61-70, 71-80, >80) to investigate how the association between hypotension and the likelihood of major adverse cardiac or cerebrovascular events and acute kidney injury differs in these age subgroups.
RESULTS: IOH was common with at least 1 reading of MAP ≤75 mm Hg occurring in 39.5% (145,743) of cases; ≤65 mm Hg in 19.3% (70,938) of cases, and ≤55 mm Hg in 7.5% (27,473) of cases. IOH was significantly associated with the primary outcome for all age groups. For an absolute maximum decrease, the estimated odds of a major adverse cardiac or cerebrovascular events in the 30-day postsurgery was increased by 12% (95% confidence interval [CI], 11-14) for ≤75 mm Hg; 17.0% (95% CI, 15-19) for ≤65 mm Hg; and by 26.0% (95% CI, 22-29) for ≤55 mm Hg.
CONCLUSIONS: IOH during noncardiac surgery is common and associated with increased 30-day major adverse cardiac or cerebrovascular events. This observation is magnified with increasing hypotension severity. The potentially avoidable nature of the hazard, and the extent of the exposed population, makes hypotension in the operating room a serious public health issue that should not be ignored for any age group.

Question: Is there an association between intraoperative hypotension and postoperative major adverse cardiac or cerebrovascular events (MACCE)?

Findings: This multicenter retrospective analysis of 368,222 surgical procedures demonstrated that regardless of how hypotension exposure was defined (ie, mean arterial pressure [MAP] ≤75, ≤65, ≤55 mm Hg), intraoperative hypotension is significantly associated with MACCE in a “dose-dependent” manner.

Meaning: Intraoperative hypotension is a potentially avoidable hazard that appears to be associated with clinically significant morbidity regardless of age group; it remains to be demonstrated whether avoidance of hypotension reduces the incidence of MACCE, and if so, whether the way normotension is maintained matters.

More than 300 million noncardiac surgeries are performed worldwide annually.[1] Intraoperative hypotension (IOH) occurs frequently during noncardiac surgery[2,3] and may be associated with organ ischemia and mortality.[4-8] Therefore, any relationship between hypotension and adverse outcomes would be of clinical importance because of the potentially modifiable nature of the hazard.

Several groups have documented a significant association between IOH and adverse postoperative outcomes.[4,6-11] A recent systematic review evaluating 42 studies for an association between IOH and adverse outcomes after noncardiac surgery reported that end-organ injury occurs when the mean arterial pressure (MAP) is <80 mm Hg for prolonged periods (≥10 minutes) and <70 mm Hg for shorter durations, although no high-quality studies were found to demonstrate specific organ injury until a threshold of MAP <65 mm Hg.[5] Additionally, the risk of end-organ dysfunction increases with progressively lower arterial blood pressure (BP).[5,11-15] According to Walsh et al,[7] as little as 1 minute of exposure to MAP <55 mm Hg is associated with myocardial injury, any cardiac complication, and kidney injury after noncardiac surgery.[7] However, currently, there are few large, multicenter studies demonstrating an association between IOH with multiple thresholds and a wide range of outcomes.[4,11]

Furthermore, a recent study found a strong relationship between IOH exposure and noncardiac surgery–related acute kidney injury (AKI) in patients <60 years of age.[16] However, another study found no independent association between varying age and postoperative AKI, without evaluating IOH exposure.[17] Therefore, we wanted to understand how the association between IOH and the likelihood of major adverse cardiac or cerebrovascular events (MACCE) and AKI differs among various age groups.

The goal of this multicenter retrospective cohort study is to assess the association between IOH and the incidence of postoperative adverse events (AE) across multiple hemodynamic thresholds in patients undergoing noncardiac surgery. We hypothesized that IOH would be associated with postoperative 30-day MACCE, defined as a composite measure of all-cause mortality, acute myocardial infarction (AMI), or acute ischemic stroke (AIS), and that this association would be magnified with increasing hypotension severity.

METHODS

Data Source

Data were obtained from the Optum (Optum, Eden Prairie, MN) deidentified electronic health records (EHR) database, which standardizes/integrates US EHRs from >2000 hospitals and 7000 clinics. The data are sourced from both ambulatory and inpatient settings, and cover diagnosis/procedure codes, clinical observations (ie, vital signs), medications, and laboratory results. This study was determined exempt from Institutional Review Board (IRB) review by Western IRB (Puyallup, WA). A statistical analysis plan, submitted to Western IRB, was made before accessing the data. This article adheres to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

Study Cohort

A total of 621,482 noncardiac/noncesarian surgeries with ≥10 minutes of intraoperative MAP recordings at an interval of ≤5 minutes, allowing for less than or equal to two 5- to 10-minute gaps between MAP readings, were identified between January 1, 2008 and December 31, 2017 based on data availability. Both a-line and cuff readings were included. There were no patient age restrictions. All MAP values were calculated using the formula: [(2 × diastolic BP {DBP}) + systolic BP (SBP)]/3. Artifactual data were removed using previously published criteria: an invalid MAP value was defined as (a) SBP ≥300 or SBP ≤20 mm Hg, (b) SBP ≤DBP +5 mm Hg, or (c) DBP ≤5 mm Hg or DBP ≥225 mm Hg; abrupt changes were removed defined by SBP change ≥80 mm Hg within 1 minute in either direction or abrupt SBP changes ≥40 mm Hg within 2 minutes in both directions.[8] The list of noncardiac surgeries were derived from procedures in the Center for Disease Control’s National Health care Safety Network Surgical Site Infection monitoring program, and surgeries were identified using the International Classification of Diseases (ICD) 9/10, and Healthcare Common Procedure Coding System codes.[18,19]

Patient procedures were excluded as follows. All patients were required to have ≥1 year of presurgical history and ≥1 touchpoint (or death) recorded in the year postindex surgery to establish baseline status and subsequent outcomes. For patients with multiple procedures within 30 days of each other, the last surgery was utilized as index procedure. In cases where qualifying surgeries were >30 days apart, all qualifying procedures were included for a patient. Procedures were excluded when (1) there were no MAP readings available in the 6 months presurgery; (2) admission and discharge were on the same day; (3) there was an invalid MAP value during the operative time; or (4) there was an invalid date of death (eg, death date/mo does not match date/mo of last follow-up visit).

Potential Confounding Variables

Patient characteristics including age, sex, race, geographic area, education level, and income were directly captured from the database. Comorbidities and procedures were identified in the year presurgery using ICD-9/10 and Current Procedural Terminology (CPT) codes (Supplemental Digital Content, Table 1, http://links.lww.com/AA/D218). Preexisting comorbidity was assessed using the Deyo adaptation of the Charlson Comorbidity Index.[20] The use of antihypertensive agents in the year presurgery was captured from patient medication history. To adjust for patient status immediately before surgery and to determine new-onset outcomes, the following conditions were identified in the 7 days before index procedure (using appropriate ICD-9/10 codes): sepsis/systemic inflammatory response syndrome (SIRS; with/without acute organ damage), delirium, electrolyte disorders, AMI, ischemic stroke, and new-onset AKI. Preexisting dialysis or continuous renal replacement therapy (CRRT) was identified over 1 year before the index procedure via ICD-9/10 codes.

The use of antihypertensive agents in the 24 hours presurgery and at the care site from which the patient was admitted (ie, home, skilled nursing facility) were directly captured from patient records. Total surgery time was estimated from the duration of high-frequency MAPs with an interval of ≤5 minutes between readings. Time of surgery (day/night—defined as after 6 pm local time) and date (weekend/weekday) were included in the models to adjust for the association between surgical (or facility) factors on the likelihood of an AE. See Tables 1 and 2 for the full list of potential confounding variables utilized and variables used to assess new onset outcomes. A sensitivity analysis to assess the magnitude of an unobserved or unaccounted confounding effect for the absolute maximum decrease (AMD) exposure was performed by calculating E values, defined as the effect required to reduce the observed odds ratio (OR) for an outcome to 1.0 (Supplemental Digital Content, Table 2, http://links.lww.com/AA/D218).[21]

Table 1.

Patient Characteristics

Patient Characteristics	Overall	IOH ≤55 mm Hg	IOH >55 mm Hg	IOH ≤65 mm Hg	IOH >65 mm Hg	IOH ≤75 mm Hg	IOH >75 mm Hg
	(n = 368,222)	(n = 27,473)	(n = 340,749)	(n = 70,938)	(n = 297,284)	(n = 145,743)	(n = 222,479)
Sex
Male	141,528 (38%)	10,402 (38%)	131,126 (38%)	25,874 (36%)	115,654 (39%)	51,725 (35%)	89,803 (40%)
Female	226,694 (62%)	17,071 (62%)	209,623 (62%)	45,064 (64%)	181,630 (61%)	94,018 (65%)	132,676 (60%)
Race
White	302,248 (82%)	22,555 (82%)	279,693 (82%)	58,948 (83%)	243,300 (82%)	122,331 (84%)	179,917 (81%)
Asian	2625 (1%)	456 (2%)	2169 (1%)	955 (1%)	1670 (1%)	1440 (1%)	1185 (1%)
Black	33,880 (9%)	1953 (7%)	31,927(9%)	4909 (7%)	28,971 (10%)	10,018 (7%)	23,862 (11%)
Other/unknown	29,469 (8%)	2509 (9%)	26,960 (8%)	6126 (9%)	23,343 (8%)	11,954 (8%)	17,515 (8%)
Age (y)
<40	44,106 (12%)	4264 (16%)	42,364 (12%)	9265 (13%)	37,773 (13%)	18,525 (13%)	25,581 (12%)
40–49	45,011 (12%)	2647 (10%)	39,842 (12%)	7238 (10%)	34,481 (12%)	15,952 (11%)	29,059 (13%)
50–59	75,057 (20%)	4963 (18%)	70,094 (21%)	12,990 (18%)	62,067 (21%)	27,126 (19%)	47,931 (22%)
60–69	94,843 (26%)	6869 (25%)	87,974 (26%)	18,486 (26%)	76,357 (26%)	38,107 (26%)	56,736 (26%)
70–79	70,304 (19%)	5379 (20%)	64,925 (19%)	14,462 (20%)	55,842 (19%)	29,413 (20%)	40,891 (18%)
80+	38,901 (11%)	3351 (12%)	35,550 (10%)	8497 (12%)	30,404 (10%)	16,620 (11%)	22,281 (10%)
Zip-3 level median household income ($)a
Median (25th, 75th percentile)	39,237 (35,981, 44,234)	40,732 (39,005, 44,234)	39,155 (35,981, 44,234)	40,550 (37,614, 44,234)	39,035 (35,981, 44,234)	39,816 (36,555, 44,234)	39,035 (35,981, 44,376)
Division
East North Central	128,631 (35%)	11,242 (41%)	117,389 (34%)	25,739 (36%)	102,892 (35%)	50,317 (35%)	78,314 (35%)
East South Central	8189 (2%)	287 (1%)	7902 (2%)	1192 (2%)	6997 (2%)	3123 (2%)	5066 (2%)
Mountain	12,592 (3%)	1049 (4%)	11,543 (3%)	2969 (4%)	9623 (3%)	5960 (4%)	6632 (3%)
Middle Atlantic	949 (0%)	172 (1%)	777 (0%)	343 (0%)	606 (0%)	519 (0%)	430 (0%)
New England	4711 (1%)	719 (3%)	3992 (1%)	1803 (3%)	2908 (1%)	2984 (2%)	1727 (1%)
Pacific	18,563 (5%)	7493 (27%)	11,070 (3%)	13,798 (19%)	4765 (2%)	16,891 (12%)	1672 (1%)
South Atlantic/West South Central	139,566 (38%)	3568 (13%)	135,998 (40%)	15,589 (22%)	123,977 (42%)	44,431 (30%)	95,135 (43%)
West North Central	46,214 (13%)	2417 (9%)	43,797 (13%)	7987 (11%)	38,227 (13%)	18,178 (12%)	28,036 (13%)
Other/unknown	8807 (2%)	526 (2%)	8281 (2%)	1518 (2%)	7289 (2%)	3340 (2%)	5467 (2%)
Charlson Comorbidity Index
0	159,261 (43%)	10,471 (38%)	148,790 (44%)	28,443 (40%)	130,818 (44%)	62,327 (43%)	96,934 (44%)
1	66,154 (18%)	4746 (17%)	61,408 (18%)	12,676 (18%)	53,478 (18%)	25,959 (18%)	40,195 (18%)
2	55,131 (15%)	4306 (16%)	50,825 (15%)	10,634 (15%)	44,497 (15%)	21,243 (15%)	33,888 (15%)
3	30,371 (8%)	2454 (9%)	27,917 (8%)	6069 (9%)	24,302 (8%)	12,042 (8%)	18,329 (8%)
4+	57,305 (16%)	5496 (20%)	51,809 (15%)	13,116 (18%)	44,189 (15%)	24,172 (17%)	33,133 (15%)
Surgery types (5 most common)b
Knee prosthesis	68,281 (19%)	2978 (11%)	65,303 (19%)	11,342 (16%)	56,939 (19%)	27,844 (19%)	40,437 (18%)
Hip prosthesis	38,160 (10%)	3286 (12%)	34,874 (10%)	9122 (13%)	29,038 (10%)	18,671 (13%)	19,489 (9%)
Open reduction of fracture	20,584 (6%)	1742 (6%)	18,842 (6%)	4064 (6%)	16,520 (6%)	8080 (6%)	12,504 (6%)
Spinal fusion	20,508 (6%)	1228 (4%)	19,280 (6%)	3047 (4%)	17,461 (6%)	6380 (4%)	14,128 (6%)
Gallbladder	20,122 (5%)	832 (3%)	19,290 (6%)	2462 (3%)	17,660 (6%)	6083 (4%)	14,039 (6%)
Year of surgery
2008–2011c	19,472 (5%)	1079 (4%)	18,393 (5%)	3115 (4%)	16,357 (6%)	7240 (5%)	12,232 (6%)
2012–2013	68,211 (19%)	2664 (10%)	65,547 (19%)	8967 (13%)	59,244 (20%)	23,005 (16%)	45,206 (20%)
2014–2015	134,398 (37%)	11,468 (42%)	122,930 (36%)	27,106 (38%)	107,292 (36%)	53,797 (37%)	80,601 (36%)
2016–2017	146,141 (40%)	12,262 (45%)	133,879 (39%)	31,750 (45%)	114,391 (38%)	61,701 (42%)	84,440 (38%)

Due to rounding, categories will not always add to 100%.

Abbreviation: IOH, intraoperative hypotension.

aThe zip-3 level median household income is generated by Optum labs. The first 3 digits of the zip code usually designate a sectional center facility and are used in this case to map salary territories at a higher level.

bSurgery types were not used for model adjustment but were used to evaluate the cumulative incidence of intraoperative hypotension for common surgeries.

cFour years were combined due to small sample size in 2008 and 2009.

Table 2.

Patient Comorbidities and Additional Characteristics

Patient Characteristics	Overall	IOH ≤55 mm Hg	IOH >55 mm Hg	IOH ≤65 mm Hg	IOH >65 mm Hg	IOH ≤75 mm Hg	IOH >75 mm Hg
	(n = 368,222)	(n = 27,473)	(n = 340,749)	(n = 70,938)	(n = 297,284)	(n = 145,743)	(n = 222,479)
Comorbidities
Myocardial infarction	25,919 (7%)	2483 (9%)	23,436 (7%)	5995 (8%)	19,924 (7%)	11,382 (8%)	14,537 (7%)
Stroke	30,113 (8%)	2800 (10%)	27,313 (8%)	6756 (10%)	23,357 (8%)	12,795 (9%)	17,318 (8%)
COPD	92,002 (25%)	7474 (27%)	84,528 (25%)	18,983 (27%)	73,019 (25%)	37,783 (26%)	54,219 (24%)
Heart failure	32,334 (9%)	3429 (12%)	28,905 (8%)	8139 (11%)	24,195 (8%)	14,864 (10%)	17,470 (8%)
Valvular heart disease	35,129 (10%)	3439 (13%)	31,690 (9%)	8148 (11%)	26,981 (9%)	15,194 (10%)	19,935 (9%)
Pulmonary circulatory disorder	11,730 (3%)	1297 (5%)	10,433 (3%)	2887 (4%)	8843 (3%)	5308 (4%)	6422 (3%)
Peripheral vascular disease	40,749 (11%)	4008 (15%)	36,741 (11%)	9774 (14%)	30,975 (10%)	18,134 (12%)	22,615 (10%)
Hypertension	230,482 (63%)	16,880 (61%)	213,602 (63%)	43,905 (62%)	186,577 (63%)	88,770 (61%)	141,712 (64%)
Paralysis	6030 (2%)	749 (3%)	5281 (2%)	1552 (2%)	4478 (2%)	2613 (2%)	3417 (2%)
Diabetes	92,799 (25%)	7085 (26%)	85,714 (25%)	17,995 (25%)	74,804 (25%)	36,233 (25%)	56,566 (25%)
Hypothyroidism	61,713 (17%)	4911 (18%)	56,802 (17%)	12,712 (18%)	49,001 (16%)	25,830 (18%)	35,883 (16%)
Renal disease	65,433 (18%)	6490 (24%)	58,943 (17%)	15,297 (22%)	50,136 (17%)	28,087 (19%)	37,346 (17%)
Liver disease	28,790 (8%)	2588 (9%)	26,202 (8%)	5997 (8%)	22,793 (8%)	11,216 (8%)	17,574 (8%)
Lymphoma	2924 (1%)	280 (1%)	2644 (1%)	645 (1%)	2279 (1%)	1219 (1%)	1705 (1%)
Solid tumor	69,582 (19%)	5960 (22%)	63,622 (19%)	13,997 (20%)	55,585 (19%)	26,640 (18%)	42,942 (19%)
RA/connective tissue disease	17,174 (5%)	1249 (5%)	15,925 (5%)	3317 (5%)	13,857 (5%)	6814 (5%)	10,360 (5%)
Coagulopathy	7951 (2%)	1082 (4%)	6869 (2%)	2233 (3%)	5718 (2%)	3767 (3%)	4184 (2%)
Obesity	106,905 (29%)	7513 (27%)	99,392 (29%)	19,041 (27%)	87,864 (30%)	38,953 (27%)	67,952 (31%)
Anemia	128,775 (35%)	11,336 (41%)	117,439 (34%)	27,844 (39%)	100,931 (34%)	54,641 (37%)	74,134 (33%)
Alcohol abuse	13,536 (4%)	1218 (4%)	12,318 (4%)	2838 (4%)	10,698 (4%)	5212 (4%)	8324 (4%)
Drug abuse	17,453 (5%)	1612 (6%)	15,841 (5%)	3768 (5%)	13,685 (5%)	6884 (5%)	10,569 (5%)
Smoking	69,048 (19%)	5018 (18%)	64,030 (19%)	13,270 (19%)	55,778 (19%)	27,063 (19%)	41,985 (19%)
Depression	88,996 (24%)	7150 (26%)	81,846 (24%)	17,672 (25%)	71,324 (24%)	35,654 (24%)	53,342 (24%)
Sleep apnea	56,316 (15%)	4459 (16%)	51,857 (15%)	10,839 (15%)	45,477 (15%)	21,521 (15%)	34,795 (16%)
Dementia	7765 (2%)	692 (3%)	7073 (2%)	1703 (2%)	6062 (2%)	3291 (2%)	4474 (2%)
Home oxygen	7610 (2%)	924 (3%)	6686 (2%)	2083 (3%)	5527 (2%)	3645 (3%)	3965 (2%)
Dialysis	6149 (2%)	908 (3%)	5241 (2%)	1914 (3%)	4235 (1%)	3076 (2%)	3073 (1%)
Coronary artery bypass graft	1609 (0%)	221 (1%)	1388 (0%)	486 (1%)	1123 (0%)	779 (1%)	830 (0%)
Percutaneous coronary intervention	2167 (1%)	220 (1%)	1947 (1%)	525 (1%)	1642 (1%)	988 (1%)	1179 (1%)
Antihypertensive medication (year presurgery)	296,965 (81%)	20,607 (75%)	276,358 (81%)	54,298 (77%)	242,667 (82%)	113,150 (78%)	183,815 (83%)
Antihypertensive medication (24 h presurgery)	151,229 (41%)	7938 (29%)	143,291 (42%)	21,778 (31%)	129,451 (44%)	50,048 (34%)	101,181 (45%)
Within 7 d before surgery
Acute myocardial infarctiona	3275 (1%)	529 (2%)	2746 (1%)	1046 (1%)	2229 (1%)	1693 (1%)	1582 (1%)
Acute ischemic strokea	4269 (1%)	431 (2%)	3838 (1%)	997 (1%)	3272 (1%)	1814 (1%)	2455 (1%)
Acute kidney injury	22,570 (6%)	2825 (10%)	19,745 (6%)	6132 (9%)	16,438 (6%)	10,586 (7%)	11,984 (5%)
Delirium	6064 (2%)	685 (2%)	5379 (2%)	1520 (2%)	4544 (2%)	2685 (2%)	3379 (2%)
Electrolyte disorder	58,010 (16%)	6365 (23%)	51,645 (15%)	14,261 (20%)	43,749 (15%)	25,448 (17%)	32,562 (15%)
Sepsis	12,925 (4%)	2293 (8%)	10,632 (3%)	4593 (6%)	8332 (3%)	7141 (5%)	5784 (3%)
Median surgery length (min)
Median (25th, 75th percentile)	30 (16, 60)	97 (50, 158)	29 (15, 55)	70 (31, 127)	25 (15, 49)	45 (22, 92)	25 (15, 45)
Date of surgery
Weekend	61,132 (17%)	5166 (19%)	55,966 (16%)	12,463 (18%)	48,669 (16%)	24,600 (17%)	36,532 (16%)
Night	23,562 (6%)	1935 (7%)	21,627 (6%)	4579 (6%)	18,983 (6%)	9132 (6%)	14,430 (6%)
Admitted from:
Home	287,335 (78%)	21,509 (78%)	265,826 (78%)	56,034 (79%)	231,301 (78%)	115,199 (79%)	172,136 (77%)
Inpatient	61,603 (17%)	4779 (17%)	56,824 (17%)	11,876 (17%)	49,727 (17%)	23,711 (16%)	37,892 (17%)
Skilled nursing facility	6981 (2%)	501 (2%)	6480 (2%)	1206 (2%)	5775 (2%)	2465 (2%)	4516 (2%)
Unknown	12,303 (3%)	684 (2%)	11,619 (3%)	1822 (3%)	10,481 (4%)	4368 (3%)	7935 (4%)

Due to rounding, categories will not always add to 100%.

aThese variables were not used for model adjustment but were used to assess patient status to determine new onset outcomes.

Abbreviations: COPD, chronic obstructive pulmonary disease; IOH, intraoperative hypotension; RA, rheumatoid arthritis.

Primary and Secondary Outcomes

The primary outcome was 30-day MACCE, defined by the composite measure of all-cause mortality, AMI, or AIS[1,22,23] (Supplemental Digital Content, Table 3, http://links.lww.com/AA/D218, for the incidence of outcomes). Death was captured from the Social Security Index while AMI and AIS were identified from ICD-9, and the corresponding ICD-10 codes identified utilizing an ICD-9 to ICD-10 medical code crosswalk[24,25] and published literature. AMI was also captured with Clinical Classifications Software diagnosis code 100.

The following secondary outcomes were evaluated: all-cause 30- and 90-day mortality, 30-day AMI, and 30-day AIS. In the 7 days following surgery, AKI was determined by either (1) using ICD-9/10 codes for AKI or (2) evaluating if the highest postoperative creatinine (within 7 days) was >1.5-fold, or >0.3 mg/dL greater than the most recent preoperative value (urine output was not utilized). The rates of new-onset intermittent hemodialysis or CRRT, delirium, and sepsis/SIRS were assessed using ICD-9/10 or CPT codes. We determined hospital-free days in the first 30 days postsurgery using inpatient EHR; patients dying in the hospital had this variable coded as zero. Primary/secondary outcomes were not limited to in-hospital events.

Determining MAP Thresholds and Exposures

The closest available MAP value to surgery was selected as baseline based on a previous study (Supplemental Digital Content, Figure 1, http://links.lww.com/AA/D218, for distribution of baseline MAPs).[26,27] Five thresholds were defined/chosen based on a review of published literature: 3 absolute (≤75, ≤65, ≤55 mm Hg) and 2 relative thresholds (20% and 40% lower than baseline).[5] We selected metrics previously identified as the most consistent across different BP measures, thresholds, and outcomes:[8,28] (a) AMD (maximum drop below specified MAP threshold) during surgery, (b) time spent under each absolute threshold value, (c) total area under each threshold-time plot, (d) time-weighted average MAP (TWA-MAP) under each threshold, and (e) the cumulative time under the prespecified relative MAP thresholds. Supplemental Digital Content, Figure 2, http://links.lww.com/AA/D218, summarizes how each hypotensive exposure was determined. The AMD exposure was chosen for the main figure for secondary outcomes because it is the easiest exposure to conceptualize/interpret and is commonly used to measure IOH exposure in the literature.[5]

IOH for the relevant threshold was defined by at least 1 MAP measurement below the threshold. All of the exposures and metrics were chosen a priori as primary exposures/metrics given the variability in IOH assessment in the published literature.[2] A systematic review revealed that 140 different IOH definitions exist in terms of what threshold defines IOH and event duration. Depending on the applied definition, the incidence of IOH can vary significantly, between 5% and 99%.[2]

Statistical Analysis

We used χ2 tests to assess unadjusted associations between patients with hypotensive events at each threshold against those with no event (Tables 1 and 2). The incidence of hypotension was reported by surgery type.

Patients who had any record of an outcome (within 7 days for AIS, AMI, AKI, delirium, or sepsis; within 1 year for dialysis or CRRT) or a procedure within the 30 days presurgery were excluded from the corresponding outcome analyses. We performed a sensitivity analysis for MACCE using a 30-day exclusion for AMI and AIS. Model covariates were defined a priori based on clinical and operative factors that might affect the estimated odds of MACCE (Tables 1 and 2). Multivariable logistic regression models were used to estimate the independent association of hypotension with primary/secondary outcomes. To assess variability across the range of IOH exposures, we plotted the mean event rate across 20 BP bins and examined the results for substantive nonlinear trends. No significant nonlinear trends were identified, and therefore, we modeled hypotensive exposures as linear predictors with each unit being a 5-mm Hg increment. Model strength was evaluated by randomly dividing the data into 80/20 testing/validation sets and evaluating the resulting c statistics from the validation datasets to assess model fit.[29]

For nondeath outcomes, death could potentially be a competing risk. Therefore, we confirmed results using Fine-Gray competing risk models for models in which mortality was not an outcome.[30] Poisson regression was used to examine the relationship between hypotensive exposures and hospital-free days in the perioperative period. Given that we examined 3 MAP thresholds for the primary analysis, the Bonferroni correction was used to adjust for multiple comparisons with a P value ≤.016 considered statistically significant.

Furthermore, to understand how the association between hypotension and the likelihood of MACCE or AKI differs among selected patient subgroups, we conducted conditional multivariable logistic regression stratified by key patient characteristics (sex, age, Charlson Comorbidity Index (CCI), surgery length, surgery type, year, time of day, and day of week), as shown in Supplemental Digital Content, Table 4, http://links.lww.com/AA/D218. Corresponding interaction P values for AMD and TWA-MAP for MACCE/AKI outcomes are provided in Supplemental Digital Content, Table 5, http://links.lww.com/AA/D218.

As a previous study found that the association between IOH and outcome may differ by age,[16] we performed a post hoc analysis to evaluate the interaction between IOH and age for the MACCE/AKI outcomes (AMD/TWA exposures) at absolute MAP thresholds. In another analysis, we stratified patients by age groups (18–40, 41–50, 51–60, 61–70, 71–80, >80) and investigated the association between IOH and MACCE/AKI outcomes (AMD/TWA exposures). Since we examined 3 MAP thresholds and 6 age groups, the Bonferroni correction was used to adjust for multiple comparisons, with a P value ≤.0028 considered statistically significant; results were reported with 99.72% confidence intervals (CI) in Supplemental Digital Content, Table 7, http://links.lww.com/AA/D218. We utilized a linear regression model to examine the trend of the odds of MACCE and AKI outcomes by age group.

All analyses were performed in SAS version 9.4 (SAS Institute Inc, Cary, NC), with all statistical tests being 2 sided. Secondary end point variable analyses were considered exploratory and thus not subjected to multiple comparison correction.

Sample Size Considerations

A previous review of 10,581,621 hospitalizations in the United States (2004–2013) found that MACCE occurred in 3.0% of major noncardiac surgeries.[1] We hypothesized at least a 3% difference in the rate of MACCE, which would require a sample size of 999 for a 95% CI and 90% power. Therefore, in our study with 621,482 procedures, we anticipate to distinguish a 0.2% difference in the primary outcome with 90% power at a 95% CI.

RESULTS

Study Cohort and Patient Characteristics

A total of 368,222 surgeries (330,827 distinct patients) met our inclusion/exclusion criteria (Figure 1).

Figure 1.

Attrition diagram. MAP indicates mean arterial pressure; OR, operating room.

The mean (±standard deviation [SD]) age of the patients was 60 ± 16.33 years (range, 1–89). A total of 98.5% (n = 363,439) of patients were ≥18 years of age. Of the study cohort, 82% were White (n = 302,248) and 62% were women (n = 226,694). The overall incidence of MACCE in the study population was 2.7%. The 30-day mortality rate was 1.7% and 90-day mortality was 2.5% (Supplemental Digital Content, Table 3, http://links.lww.com/AA/D218, for incidence of outcomes). Descriptive statistics comparing patients with hypotensive events to those with no event and the overall cohort are shown in Tables 1 and 2.

Association Between IOH and Outcomes

Overall, 19.3% of patients experienced a hypotensive event defined as at least 1 MAP ≤65 mm Hg reading. Supplemental Digital Content, Figure 3, http://links.lww.com/AA/D218, describes the incidence of IOH across the most common noncardiac surgeries performed during the study period.

The adjusted ORs with 95% CIs for the regression models with all exposure methods are depicted in Figure 2A, B. For AMD, every 5-mm Hg decrease in MAP ≤65 mm Hg is associated with an estimated 17% increase in odds of MACCE (95% CI, 15-19; Figure 2A) and in MAP ≤55 mm Hg, it is associated with an estimated 26% increase in the odds of MACCE (95% CI, 22-29).

Figure 2.

Adjusted ORs for the likelihood of adverse clinical events (A) for the primary outcome using all absolute exposure methods and (B) for the primary outcome using relative cumulative time (C) for the secondary outcomes for the AMD for each 5 mm Hg under IOH MAP thresholds. A, The adjusted ORs with 95% CIs for the regression models for MACCE for the following IOH exposures: AMD in MAP during surgery (maximum drop below specified MAP threshold), time spent under each absolute threshold value, total area under each threshold-time plot, TWA-MAP under each threshold during the study period are shown. B, The adjusted ORs with 95% CIs for the regression models for MACCE for the IOH exposure of cumulative time under the prespecified relative MAP thresholds during the study period are shown. C, The adjusted ORs with 95% CIs for the regression models for secondary outcomes with AMD in MAP during the study period are shown. Adj. indicates adjusted; AIS, acute ischemic stroke; AKI, acute kidney injury; AMD, absolute maximum decrease; AMI, acute myocardial infarction; CI, confidence interval; CRRT, continuous renal replacement therapy; IOH, intraoperative hypotension; MACCE, major adverse cardiac or cerebrovascular events; MAP, mean arterial pressure; OR, odds ratio; SIRS, systemic inflammatory response syndrome; TWA, time-weighted average.

The post hoc analysis revealed a significant interaction between IOH and age when evaluated for MACCE/AKI outcomes for MAP thresholds ≤75, ≤65, ≤55 mm Hg for AMD (P < .001, all thresholds) and TWA (P < .001 for ≤55 and ≤65-mm Hg thresholds; ≤75-mm Hg threshold: P < .008, MACCE; P < .005, AKI). A subgroup analysis showed an association between IOH and MACCE for all age groups when evaluated by AMD (Figure 3) and TWA-MAP.

Figure 3.

Adjusted ORs for the likelihood of MACCE for each 5 mm Hg under IOH MAP thresholds, by age group. The adjusted ORs with 95% CIs for the regression models with AMD in MAP during the study period are shown. ORs are plotted on the log-OR scale but labeled on the OR scale. Of note due to rounding some of the CIs appear to be asymmetrical or not visible. N for each age group by threshold: MAP ≤75 mm Hg: 18–40, N = 16,390; 41–50, N = 16,757; 51–60, N = 28,724; 61–70, N = 38,108; 71–80, N = 27,767; >80, N = 14,641; MAP ≤65 mm Hg: 18–40, N = 7466; 41–50, N = 7676; 51–60, N = 13,783; 61–70, N = 18,483; 71–80, N = 13,651; >80, N = 7553); MAP ≤55 mm Hg: 18–40, N = 2931; 41–50, N = 2843; 51–60, N = 5217; 61–70, N = 6866; 71–80, N = 5093; >80, N = 2994). Adj. indicates adjusted; AMD, absolute maximum decrease; CI, confidence interval; IOH, intraoperative hypotension; MACCE, major adverse cardiac or cerebrovascular events; MAP, mean arterial pressure; OR, odds ratio.

Descriptive statistics for the subgroup analysis and adjusted ORs, stratified by age group, for the association between IOH and MACCE/AKI outcomes evaluated by AMD and TWA-MAP metrics, are provided in Supplemental Digital Content, Tables 6–7, http://links.lww.com/AA/D218. The estimated odds for association of AMD-IOH with MACCE among adults age 18–40 was higher than for the oldest adults >80 years for both MAP ≤75-mm Hg (18–40 years: OR = 1.27, [99.72% CI, 1.16-1.39]; >80 years: OR = 1.10, [99.72% CI, 1.07-1.14]) and MAP ≤65-mm Hg thresholds (18–40 years: OR = 1.37, [99.72% CI, 1.18-1.58]; >80 years: OR = 1.14, [99.72% CI, 1.07-1.21]). Linear regression models demonstrated that the association between IOH and MACCE decreased as age increased (≤75 mm Hg P = .024, ≤65 mm Hg P = .020, ≤55 mm Hg P = .010); however, no association was found for AKI (≤75 mm Hg P = .387, ≤65 mm Hg P = .137, ≤55 mm Hg P = .095). Thus, when adjusted for covariates, the risk associated with IOH among the oldest patients (>80 years) is less than for patients 18–40 years, indicating that other factors likely affect MACCE events among older patients.

AMD below all MAP thresholds examined was associated with all secondary outcomes except for AIS (Figure 2C). Specifically, AMD below all hypotensive thresholds was correlated with 30- and 90-day mortality, perioperative AMI, AKI, CRRT, postoperative delirium, and sepsis/SIRS (Figure 2C). A sensitivity analysis incorporating 30-day exclusion criteria for AMI/AIS provided similar results (data not shown). E value results evaluating unmeasured confounders required to reduce the OR to 1.0 are located in Supplemental Digital Content, Table 2, http://links.lww.com/AA/D218. Calculated E values for AMD-MAP for significant primary and secondary outcomes ranged from 1.92 to 1.24 (MACCE ≤55-mm Hg threshold to delirium ≤75-mm Hg threshold).

Sensitivity analyses showed similar results using competing risk models for nonmortal secondary end points (data not shown) or using different IOH measurements including TWA-MAP, total area under each threshold-time plot, the time spent under each absolute threshold value, and cumulative time under the prespecified relative MAP thresholds (Supplemental Digital Content, Figure 4, http://links.lww.com/AA/D218).

For all absolute MAP thresholds, and under the 40% below baseline relative MAP threshold, IOH was associated with MACCE; P < .001 (Table 3). There was no significant association between time under the relative threshold of 20% below baseline MAP and MACCE (Table 3). Across all thresholds, AMD-IOH was associated with fewer hospital-free days (MAP ≤75 mm Hg, incidence rate ratio [IRR] 0.99 [95% CI, 0.99-0.99]; MAP ≤65 mm Hg, IRR 0.99 [95% CI, 0.98-0.99]; MAP ≤55 mm Hg, IRR 0.97 [95% CI, 0.97-0.98]).

Table 3.

ORs for IOH for Risk of MACCE

	IOH Threshold
	≤55 mm Hg		≤65 mm Hg		≤75 mm Hg		≥20% Below Baseline		≥40% Below Baseline
Mean time under threshold (min) (SD)	12 (24)		22 (34)		32 (44)		32 (44)		17 (27)
Mean lowest MAP under threshold (SD)	47 (10)		55 (10)		63 (10)		67 (13)		53 (10)
Exposure Measures OR and P Values for MACCE
	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
AMD below specified MAP threshold (for 5 mm Hg)	1.26 (1.22-1.29)	<.001	1.17 (1.15-1.19)	<.001	1.12 (1.11-1.14)	<.001
Area (per 5 mm Hg × 5 min)	1.02 (1.02-1.03)	<.001	1.01 (1.01-1.02)	<.001	1.01 (1.01-1.01)	<.001
Cumulative time (per 5 min)	1.05 (1.04-1.06)	<.001	1.03 (1.03-1.04)	<.001	1.02 (1.02-1.03)	<.001	1.00 (1.00-1.01)	.99	1.02 (1.00-1.03)	.01
TWA-MAP (per 5 mm Hg)	1.14 (1.12-1.16)	<.001	1.07 (1.06-1.08)	<.001	1.03 (1.03-1.04)	<.001

Grey fields mean not applicable for this IOH threshold.

Abbreviations: AMD, absolute maximum decrease (below specified MAP threshold); CI, confidence interval; IOH, intraoperative hypotension; MACCE, major adverse cardiac or cerebrovascular events; MAP, mean arterial pressure; OR, odds ratio; SD, standard deviation; TWA, time-weighted average.

DISCUSSION

We report an association between hypotension during noncardiac surgery and increased occurrence of major clinical AEs, namely death, myocardial infarction, and stroke. We studied 368,222 noncardiac surgical procedures from the Optum EHR database, making this the largest multicenter retrospective cohort study of IOH outcomes to date, and analyzing a range of outcomes across multiple hemodynamic thresholds and definitions. The association between IOH and MACCE was assessed using 5 different methods, and all 5 methods demonstrated a consistent and significant association between IOH and 30-day MACCE (Table 3). There does not appear to be a safe duration of time for exposure to MAP ≤75 mm Hg. We also examined the associations between IOH and MACCE for varying age groups, which is novel to this study.

Our findings demonstrate that there is a progressive increased association for each absolute MAP threshold examined (≤75, ≤65, ≤55 mm Hg) for MACCE and 30- and 90-day mortality. Furthermore, with the exception of stroke, the estimated odds for the association of AMD thresholds were greater than unity for an increase in all secondary outcomes (only delirium at MAP ≤55-mm Hg threshold had a lower 95% CI value of 1.0). The apparent lack of a statistically significant association with AIS in our analysis is likely reflective of the low incidence (0.68%) in our population. Our study demonstrated an increased association between hypotensive events and delirium, which is in contrast to a previous study that examined a specific hypotensive anesthesia protocol where no significant association between IOH and delirium or cardiac/renal complication was found.[31] One study showed no causality between IOH and 1-year mortality, except in the elderly; however, this is a longer timeframe than the 30- and 90-day mortality defined here.[32] Our findings are consistent with a recent systematic review that investigated the association between IOH and postoperative outcomes, but which cautioned about interstudy differing patient characteristics and exposure definitions. The authors found that prolonged exposure (≥10 minutes) to MAP <80 mm Hg and for shorter durations, <70 mm Hg, was associated with mildly increased risk of end-organ injury.[5] Increased time with MAP <65 mm Hg or any exposure to MAP <55 mm Hg was significantly associated with moderately- or highly-elevated postoperative risk.[5] Previous analyses support our observation that even short periods of IOH below MAP thresholds of 55–65 mm Hg are associated with significant postoperative AEs. Futier et al[33] recently reported results from a randomized trial that found that maintaining SBP within 10% of the reference value may prevent postoperative organ damage compared to standard care (treating SBP <80 mm Hg or <40% of the reference value). Taken together, our study and these data suggest that IOH has an important, and avoidable, association with postoperative 30-day MACCE.

We also show that IOH exposure is associated with postoperative MACCE and AKI across various age groups. In fact, higher adjusted ORs in the younger patient groups suggest that the estimated odds of AE associations of IOH exposure are as least as high as in older patients. Specifically, patients 18–40 years had a significantly higher estimated odds for MACCE associated with IOH exposure than patients >80 years of age. Tang et al[16] also reported a strong relationship between IOH exposure and noncardiac surgery–related AKI in patients <60 years old. Mathis et al[17] explored the relationship between varying age and AKI and found no independent association without evaluating exposure to IOH. To our knowledge, this is the first study that evaluated the association between IOH exposure and MACCE in various age groups. There may be 2 main reasons for lower ORs in older patients: (1) we controlled for various risk factors that are more likely to affect older patients (ie, Charlson Comorbidity Index), which may have led to a slightly less incremental risk, and (2) older patients may tolerate lower perfusion pressures better due to multiple, transient, brief episodes of ischemia (ischemic preconditioning) over their lifespan compared to younger patients.[34] Overall, our findings clearly show that IOH matters in all patients, old or young, and, therefore, should not be ignored in any age group.

We also characterized IOH exposure by cumulative time beneath relative MAP thresholds and found an association between MACCE and time spent with MAP ≥40% below the baseline; however, the relative thresholds were less sensitive in detecting an association with MACCE compared to absolute thresholds. The relationship of IOH with postoperative outcomes regarding relative decreases from baseline BP in chronically hypertensive patients is debated among anesthesiologists.[35] Salmasi et al[8] found that IOH (MAP <65 mm Hg) was associated with myocardial or kidney injury in noncardiac surgery patients, and also performed a detailed comparison between absolute (MAP <65 mm Hg) and relative (decrease of >25% below preoperative baseline) thresholds and demonstrated no advantage to using relative over absolute thresholds. Absolute thresholds provide multiple advantages: (1) baseline BP measurements are frequently unknown (in a study by Monk et al,[4] 31% of patients did not have a baseline BP measurement recorded); (2) absolute thresholds are easier to incorporate into a decision-making framework that may not have access to preoperative BP measurements for individual patients; and (3) preoperative readings are often elevated due to patients experiencing anxiety presurgery. This may explain our finding that a reduction of 40% below baseline is more likely to represent a true effect, while a 20% decrease from the preoperative readings might be caused by a temporary increase in BP immediately presurgery.

This study has several limitations. While the dataset contains data from centers throughout the United States, it is relatively under-representative of the geographical West and Northeast regions. Additionally, race distribution does not reflect US national demographics. We were limited to estimating surgical duration by using the time period in which high-frequency MAP readings were available. Therefore, we conducted sensitivity analyses to confirm the consistency of our findings across different surgery durations. Although we only included MAP readings with intervals of <5 minutes, allowing a maximum of 2 gaps of 5–10 minutes between MAP readings, we did not lose significant fidelity of measurement. Furthermore, a single preoperative MAP reading might not be representative of the true patient baseline BP. We captured outcomes using administrative codes, which may not represent the true number of events. However, we removed patients with an immediate prior history of key outcomes to increase the likelihood of capturing new-onset AEs; moreover, we provided a sensitivity analysis with a longer timeline for the AMI and AIS outcomes. Due to database limitations, we could not capture and verify the cause of death. The postoperative use of antihypertensives was not evaluated. Specific diagnosis criteria for health conditions may be variable across/within institutions. The risk of individual site biases in reporting was likely mitigated by our sample size, and our event rate for the primary outcome is consistent with previous publications in noncardiac surgery. We utilized a Bonferroni correction of P ≤ .0028 and 99.72% CI for the likelihood of MACCE and AKI with hypotension stratified by age which corresponded to our 3 MAP thresholds and 6 age groups, but did not take into account the number of outcomes or the number of exposures evaluated (ie, AMD and TWA). Finally, this is an observational, retrospective study, for which exists the risk of residual confounding and unobservable bias, although we have attempted to control for observable patient and surgical factors and provided E values for unobserved confounding (provides the strength of association that an unmeasured confounder must have to reduce the observed association to unity). Calculated E values for MACCE were 1.83–1.49 (≤55–≤75-mm Hg threshold), indicating that an unmeasured confounder with this association strength would be required to change our conclusion. This study can only comment on the association between IOH and outcomes, which highlights the need for further prospective research to investigate the associations between reducing patient exposure to IOH and AEs.

In conclusion, we report an association between IOH and increased MACCE. The relationship is “dose-dependent,” with no apparent safe period of hypotension. Given the potentially avoidable nature of the hazard and the extent of the exposed population, we believe hypotension in the operating room is a serious public health issue, and should not be ignored in any age group. We suggest there is an urgent and currently unmet need for prospective interventional studies focused on its prevention.

ACKNOWLEDGMENTS

The authors thank Sibyl H. Munson, PhD, Halit O. Yapici, MD, MPH, MBA, and Francie Moehring, PhD, at Boston Strategic Partners, Boston, MA (supported by Edwards Lifesciences) for editorial support.

DISCLOSURES

Name: Anne Gregory, MD, MSc, FRCPC.

Contribution: This author helped design the study, supervise the data collection, analyze the data, and prepare the manuscript.

Conflicts of Interest: None.

Name: Wolf H. Stapelfeldt, MD.

Contribution: This author helped design the study, supervise the data collection, analyze the data, and prepare the manuscript.

Conflicts of Interest: W. H. Stapelfeldt received consulting fees from Edwards Lifesciences.

Name: Ashish K. Khanna, MD, FCCP, FCCM.

Contribution: This author helped design the study, supervise the data collection, analyze the data, and prepare the manuscript.

Conflicts of Interest: A. K. Khanna received consulting fees from Edwards Lifesciences and is supported by a NIH/NCATS Wake Forest University CTSI KL2 award TR001421 for a pilot trial of continuous portable postoperative hemodynamic and saturation monitoring on the hospital ward.

Name: Nathan J. Smischney, MD, MSc.

Contribution: This author helped design the study, supervise the data collection, analyze the data, and prepare the manuscript.

Conflicts of Interest: N. J. Smischney received consulting fees from Edwards Lifesciences.

Name: Isabel J. Boero, MD, MS.

Contribution: This author helped design the study, supervise the data collection, analyze the data, and prepare the manuscript.

Conflicts of Interest: I. J. Boero is an employee of Boston Consulting Group, who received funds from Edwards Lifesciences to perform the research.

Name: Qinyu Chen, MS.

Contribution: This author helped analyze the data and prepare the manuscript.

Conflicts of Interest: Q. Chen is an employee of Boston Consulting Group, who received funds from Edwards Lifesciences to perform the research.

Name: Mitali Stevens, PharmD, BCPS.

Contribution: This author helped analyze the data and prepare the manuscript.

Conflicts of Interest: M. Stevens is an employee of Edwards Lifesciences.

Name: Andrew D. Shaw, MB, FRCPC.

Contribution: This author helped design the study, supervise the data collection, analyze the data, and prepare the manuscript.

Conflicts of Interest: A. D. Shaw received consulting fees from Edwards Lifesciences.

This manuscript was handled by: Tong J. Gan, MD.